چكيده به لاتين
Graph is used in a variety of applications for modeling data and their relationships. Social networks, communication networks, web graphs, biological networks and chemical compounds are examples of data modeled by graphs. These days, many applications generate large scale and massive graphs with billions of nodes and edges and many research works have been done on the theory and engineering of tera-scale graphs. Graph summarization has been proposed as a solution for processing massive graphs. Graph summarization algorithms reduce a massive graph into a smaller one by removing its details but preserving its overall properties. Graph summarization can be structural, attributed-absed or based on both the structure and vertex attributes if the graph is an attributed graph. There are a number of algorithms for structural summarization and we have proposed a new method for structural summarizing that constructs a summary with a better quality. Summarization a graph based on both the structure and the vertex attributes is challenging. Although generating an attribute-based summary is not a hard problem and there are a number of algorithms this purpose, but generating a summary based on both the graph structure and vertex attributes (hybrid summarization) with the user-specified contributions of the structure and vertex attributes is not easy and this is the main challenge of graph summarization. It is obvious that the importance of structure and vertex attributes in the resultant summary is not the same in all applications and therefore considering variable weighting factors for them is more reasonable. Recently two algorithms have been proposed for hybrid summarization/clustering of a graph. In this dissertation, the aim has been proposing a new ontology-based method for summarizating an attributed graph stream. A new algorithm has been proposed for graph stream summarization based on sliding window paradigm. Of-course we have proposed new algorithms for structural summarization and hybrid summarization of stationery attributed graph. Our contributions are: 1- Proposing a new method for summarizing structural graph: In fact, we have improved the criterion of selecting the best super-node for division. 2- Proposing a new method for summarizing attributed graphs: we have proposed a new method for summarizing attributed graphs based on both the structure and vertex attributes. 3- A new method has been proposed for summarizing an attributed graph stream
